Star Formation Rates in [ne V] 3426 Å Selected Active Galactic Nuclei: Evidence for a Decrease along the Main Sequence?
The Astrophysical Journal(2024)SCI 2区SCI 3区
Catholic Univ Amer | Space Telescope Sci Inst | Naval Res Lab
Abstract
Studying the behavior along the galaxy main sequence is key in furthering our understanding of the possible connection between active galactic nucleus (AGN) activity and star formation. We select a sample of 1215 AGN from the catalog of Sloan Digital Sky Survey galaxy properties from the Portsmouth group by detection of the high-ionization [Ne v ] 3426 Å emission line. Our sample extends from 10 ^40 to 10 ^42.5 erg s ^−1 in [Ne v ] luminosity in a redshift range z = 0.17 to 0.57. We compare the specific star formation rates (sSFRs; SFR scaled by galaxy mass) obtained from the corrected [O ii ] and H α luminosities, and the spectral energy distribution (SED)–determined values from Portsmouth. We find that the emission-line-based sSFR values are unreliable for the [Ne v ] sample due to the AGN contribution, and proceed with the SED sSFRs for our study of the main sequence. We find evidence for a decrease in sSFR along the main sequence in the [Ne v ] sample, which is consistent with results from the hard X-ray Burst Alert Telescope AGN sample, which extends to lower redshifts than our [Ne v ] sample. Although we do not find evidence that the concurrent AGN activity is suppressing star formation, our results are consistent with a lower gas fraction in the host galaxies of the AGN as compared to that of the star-forming galaxies. If the evacuation of gas, and therefore suppression of star formation, is due to AGN activity, it must have occurred in a previous epoch.
MoreTranslated text
Key words
Active galaxies,Starburst galaxies,Star formation
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话